Final Portfolio

Preamble

This project aims to allow the user to interactively look at infection rates of the most prevalent STI’s: Chlamydia, Gonorrhea, and Syphilis. By using this portfolio you will be able to mouse over a map of the united states for each infection and visualize data from the CDC on infection rates per county.

Data

Code

suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(scales))
suppressPackageStartupMessages(library(rnaturalearthdata))
suppressPackageStartupMessages(library(rnaturalearth))
suppressPackageStartupMessages(library(sf))
suppressPackageStartupMessages(library(tigris))
library(tidyverse)
library(dplyr)
library(ggplot2)
library(readxl)
library(scales)
library(rnaturalearth)
library(rnaturalearthdata)
library(sf)
library(tigris)

suppressPackageStartupMessages(library(tigris))

#STIDictionary<-read_excel("STISheet.xlsx")
#knitr::kable(STIDictionary)

Code

data <- read.csv("Chlamydia - Rates of Reported Cases by County United States 2021 .csv")

Visualizations

Each of these visualizations is an interactive, spatial, heat-map of the United States. By mousing over individual counties it will show you the county name and infection rate.

Code

library(sf)
library(ggplot2)
library(dplyr)
library(ggiraph)

data <- read.csv("Chlamydia - Rates of Reported Cases by County United States 2021 .csv", header = TRUE)
data$Rate <- as.numeric(data$Rate, na.rm = TRUE)

Warning: NAs introduced by coercion

Code

invisible(suppressWarnings({
  us_counties <- tigris::counties(cb = TRUE, resolution = "20m", year = 2020, class = "sf", progress = FALSE)
}))



us_counties_contiguous <- us_counties %>% 
  filter(
    !(STATEFP %in% c("02", "15", "60", "66", "69", "72", "78"))
  )

us_counties_data <- left_join(us_counties, data, by = c("NAMELSAD" = "County"), relationship = "many-to-many")
us_counties_data$Rate <- as.numeric(us_counties_data$Rate)

us_counties_data_contiguous <- us_counties_data %>% 
  filter(
    !(STATEFP %in% c("02", "15", "60", "66", "69", "72", "78"))
  )

my_colors <- c('blue', 'purple', 'red', 'orange', 'yellow')


gg <- ggplot() +
  geom_sf_interactive(data = us_counties_data_contiguous, aes(fill = Rate, tooltip = paste(NAMELSAD, "<br>", "Rate:", Rate)), color = "grey", size = 0.1) +
  scale_fill_gradientn(colors = my_colors, na.value = "grey70", name = "Rate") + 
  labs(title = "Chlamydia Infection Rate by County 2021", caption = "Total infection rate by chlamydia by county") +
  theme_minimal() +
  theme(axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank(),
        plot.caption = element_text(hjust = .5, size = 8, margin = margin(t = 10, r = 10)))


girafe(code = print(gg))

Code

library(sf)
library(ggplot2)
library(dplyr)
library(ggiraph)

data2 <- read.csv("Gonorrhea - Rates of Reported Cases by County United States 2021 .csv", header = TRUE)
data2$Rate <- as.numeric(data2$Rate, na.rm = TRUE)

Warning: NAs introduced by coercion

Code

invisible(suppressWarnings({
  us_counties <- tigris::counties(cb = TRUE, resolution = "20m", year = 2020, class = "sf", progress = FALSE)
}))


us_counties_contiguous <- us_counties %>% 
  filter(
    !(STATEFP %in% c("02", "15", "60", "66", "69", "72", "78"))
  )

us_counties_data <- left_join(us_counties, data2, by = c("NAMELSAD" = "County"), relationship = "many-to-many")
us_counties_data$Rate <- as.numeric(us_counties_data$Rate)

us_counties_data_contiguous <- us_counties_data %>% 
  filter(
    !(STATEFP %in% c("02", "15", "60", "66", "69", "72", "78"))
  )

my_colors <- c('blue', 'purple', 'red', 'orange', 'yellow')


gg <- ggplot() +
  geom_sf_interactive(data = us_counties_data_contiguous, aes(fill = Rate, tooltip = paste(NAMELSAD, "<br>", "Rate:", Rate)), color = "grey", size = 0.1) +
  scale_fill_gradientn(colors = my_colors, na.value = "grey70", name = "Rate") + 
  labs(title = "Gonorrhea Infection Rate by County 2021", caption = "Total infection rate by gonorrhea by county") +
  theme_minimal() +
  theme(axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank(),
        plot.caption = element_text(hjust = .5, size = 8, margin = margin(t = 10, r = 10)))


girafe(code = print(gg))

Code

library(sf)
library(ggplot2)
library(dplyr)
library(ggiraph)

data3 <- read.csv("Primary and Secondary Syphilis - Rates of Reported Cases by County United States 2021 .csv", header = TRUE)
data3$Rate <- as.numeric(data3$Rate, na.rm = TRUE)

Warning: NAs introduced by coercion

Code

invisible(suppressWarnings({
  us_counties <- tigris::counties(cb = TRUE, resolution = "20m", year = 2020, class = "sf", progress = FALSE)
}))


us_counties_contiguous <- us_counties %>% 
  filter(
    !(STATEFP %in% c("02", "15", "60", "66", "69", "72", "78"))
  )

us_counties_data <- left_join(us_counties, data3, by = c("NAMELSAD" = "County"), relationship = "many-to-many")
us_counties_data$Rate <- as.numeric(us_counties_data$Rate)

us_counties_data_contiguous <- us_counties_data %>% 
  filter(
    !(STATEFP %in% c("02", "15", "60", "66", "69", "72", "78"))
  )

my_colors <- c('blue', 'purple', 'red', 'orange', 'yellow')


gg <- ggplot() +
  geom_sf_interactive(data = us_counties_data_contiguous, aes(fill = Rate, tooltip = paste(NAMELSAD, "<br>", "Rate:", Rate)), color = "grey", size = 0.1) +
  scale_fill_gradientn(colors = my_colors, na.value = "grey70", name = "Rate") + 
  labs(title = "Primary and Secondary Syphilis Infection Rate by County 2021", caption = "Total infection rate by primary and secondary Syphilis by county") +
  theme_minimal() +
  theme(axis.text = element_blank(),
        axis.title = element_blank(),
        axis.ticks = element_blank(),
        panel.grid = element_blank(),
        plot.caption = element_text(hjust = .5, size = 8, margin = margin(t = 10, r = 10)))


girafe(code = print(gg))

Conclusion

Chlamydia has the highest rate of infection out of the three STI’s, though gonorrhea follows a similar pattern of infection rate by county. Syphilis, the lowest infection rate of the three seems to have a few hot spots but it’s hard to say if there is a pattern.